Abstract:Tagging photos with relevant keywords is an important step in making large collections of photos like Flickr or Facebook easily accessible and searchable. The text tags provided by Flickr users for their photos are often very noisy. Users may tag photos with incorrect information or with information that is not relevant to the visual content of the image. For example, a photo of the Statue of Liberty or The Mona Lisa may be labeled "holiday" because the owner of the photo was on vacation when they created it, but this tag is not useful for locating the object depicted in the photo. Another significant problem is that many images are not tagged at all and are therefore inaccessible by text-based search queries.

The goal is to filter out the irrelevant tags associated with Flickr images and then to automatically suggest relevant tags for previously untagged images making them accessible to text queries. The tag cleaning step is done by an automated method based on the ideas behind to the manual image tagging game ESP. The auto annotation step is based on collecting tags assigned to other images that have visually similar parts detected by local feature matching techniques.

Bio:Kyle Heath is a Ph.D. student in the Department of Electrical Engineering at Stanford University. His work with Prof. Leonidas Guibas has focused on wireless camera sensor networks, a field which lies at the intersection of low-power wireless sensing and computer vision. He has developed lightweight distributed computer vision algorithms for tracking moving objects in cluttered environments on resource-constrained wireless sensor platforms. Recent research has focused on methods for organizing and mining information from large image collections.

Kyle holds an M.S. degree in Electrical Engineering from Stanford and a B.S. degree in Computer Engineering from Rose-Hulman Institute of Technology. As a Rose-Hulman undergraduate, he enjoyed building robots for autonomous aerial robotics competitions.